Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering

CSF images fast recognition model based on improved convolu-tional Neural Network

Authors
Wenming Huang, Jinqiang Leng, Zhenrong Deng
Corresponding Author
Wenming Huang
Available Online April 2015.
DOI
https://doi.org/10.2991/amcce-15.2015.97How to use a DOI?
Keywords
convolution neural network; cerebrospinal fluid; rectie function; identification; classification of linear support vector machine
Abstract
The sparseness of feature is an important characteristic determining feature, which directly affects the accuracy of image recognition[1]. By studying the traditional con-volution neural network, we find that the learning of image features of cerebrospinal fluid cell easily overfits, but using rectie activation function instead of sigmoid activa-tion functions, the features extracted are more sparse and have faster convergence rate in the process of training. Then features extracted are classified through a linear sup-port vector machine. The experiments show that the improved model can enhance significantly the image recognition efficiency of cerebrospinal fluid, where two, three, four categories are respectively increased by 9.78%, 6.53%, 11.69%, and the average recognition time of a single image is also reduced 0.32s.
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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2015
ISBN
978-94-62520-64-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/amcce-15.2015.97How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wenming Huang
AU  - Jinqiang Leng
AU  - Zhenrong Deng
PY  - 2015/04
DA  - 2015/04
TI  - CSF images fast recognition model based on improved convolu-tional Neural Network
PB  - Atlantis Press
SP  - 518
EP  - 524
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.97
DO  - https://doi.org/10.2991/amcce-15.2015.97
ID  - Huang2015/04
ER  -